Data Models, Coordinates, and Geometries
Content for Monday, September 15, 2025
Now that you’ve gotten an introduction (or refresher) to the logic of tidy data and the tidyverse, we can move into the actual spatial portion of the class! The first step in doing that is understanding how/why spatial data differs from more traditional tabular datasets.
Readings
Setting the Stage
Data Types and Spatial Data Models by Skeeter and Pickell provides a general overview of different data types and how we might think about storing them spatially.
Types of Spatial Data from Paula Moraga’s new book Spatial Statistics for Data Science: Theory and Practice with R provides a nice overview of the types of spatial data from the perspective of a statistical analyst.
Attributes and Support from Pebesma and Bivand’s Spatial Data Science with Applications in R gives more info and examples on the nature of the relationship between geometries and support.
Technical Details
- The introductory vignette for the
sfpackage has a lot of useful info onsfobjects and conventions.
Chapter 2 in Geocomputation with R (Lovelace et al. 2019) provides and overview of using sf for vector datasets and terra for raster data.
Objectives
By the end of today you should be able to:
Contrast the different “views” of spatial data and their incorporation in GIS.
Identify key elements that make data “spatial”.
Recognize the relationship between geometries and support.
Access geographic attributes of spatial objects using
sfandterra
Slides
The slides for today’s lesson are available online as an HTML file. Use the buttons below to open the slides either as an interactive website or as a static PDF (for printing or storing for later). You can also click in the slides below and navigate through them with your left and right arrow keys.